FIA NLS Models: Biomass Growth vs. Biomass – Data Subsets
JAH
2022-12-14
Background
This analysis document compliments FIA NLS Models: Biomass Growth vs. Biomass. All of the background information from that document applies to these analyses, which are extensions to them. The difference between that document and this analysis is the use of different data subsets.
Here, we fit the models using: 1) a temporally-balanced dataset, where we take the first and most-recent plot record for all plots in the dataset, 2) a temporally-balanced dataset (same as #1), but which excludes plot locations which have experienced harvest (at any point over the study interval 2000-2022)
Below the model fitting procedure is implemented by ecoprovince:
Temporally-balancing the biomass growth data set
Lets look at some quick attributes of the dataset
- The data set has 115221 observations, comprised of 58079 plots.
- The frequency of growth measurements among plots is as follows (n=1 through 5): 25558, 13784, 12967, 5656, 114.
- Thus 55.99% of plots have at least two growth measurements.
Analysis 1: Temporally-balanced analysis
211 - Northeastern Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 4795 4394.3
## 2 4794 4389.9 1 4.362 4.7636 0.02912 *
## 3 4793 4205.8 1 184.087 209.7877 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 18612.85
## 2 2 18610.08
## 3 3 18406.54##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 0.017420 0.175129 0.099 0.9208
## phi 0.009372 0.005673 1.652 0.0986 .
## alpha 0.630415 0.040841 15.436 <2e-16 ***
## A 3.616767 0.127197 28.434 <2e-16 ***
## k 7.358455 0.782101 9.409 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9367 on 4793 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 2.987e-06
## (36 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
model selection 2
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (p * A + ((1 - p) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)))
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (A * B_plt_t1_MgHa^s/(k^s + B_plt_t1_MgHa^s))
## Model 4: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (p * A + ((1 - p) * A * B_plt_t1_MgHa^s/(k^s + B_plt_t1_MgHa^s)))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 4793 4205.8
## 2 4792 4197.9 1 7.8859 9.0019 0.002711 **
## 3 4792 4196.7 0 0.0000
## 4 4791 4196.7 1 0.0049 0.0056 0.940386
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 3 18406.54
## 2 3a 18399.54
## 3 3b 18398.14
## 4 3c 18400.13##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (A * B_plt_t1_MgHa^s/(k^s +
## B_plt_t1_MgHa^s))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 0.021936 0.175098 0.125 0.90
## phi 0.008568 0.005660 1.514 0.13
## alpha 0.626665 0.040848 15.341 < 2e-16 ***
## A 4.060029 0.280764 14.461 < 2e-16 ***
## k 7.729586 1.548545 4.992 6.20e-07 ***
## s 0.604862 0.108904 5.554 2.94e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9358 on 4792 degrees of freedom
##
## Number of iterations to convergence: 5
## Achieved convergence tolerance: 5.013e-06
## (36 observations deleted due to missingness)summary
- add p model: fits
- add s model: fits
- add s+p model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.92609, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -8.5575, p-value < 2.2e-16
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 21 rows containing missing values (geom_point).## Warning: Removed 1050 row(s) containing missing values (geom_path).
plotting 2
212 - Laurentian Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 9777 9697.8
## 2 9775 9681.2 2 16.60 8.3794 0.0002312 ***
## 3 9774 9053.6 1 627.57 677.5012 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 35844.15
## 2 2 35826.64
## 3 3 35173.25##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 0.905672 0.202832 4.465 8.09e-06 ***
## phi 0.019056 0.004331 4.400 1.09e-05 ***
## alpha 0.811354 0.028498 28.471 < 2e-16 ***
## A 2.661009 0.095088 27.985 < 2e-16 ***
## k 10.143277 0.592955 17.106 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9624 on 9774 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 7.105e-06
## (3197 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
model selection 2
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (p * A + ((1 - p) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)))
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (A * B_plt_t1_MgHa^s/(k^s + B_plt_t1_MgHa^s))
## Model 4: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (p * A + ((1 - p) * A * B_plt_t1_MgHa^s/(k^s + B_plt_t1_MgHa^s)))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 9774 9053.6
## 2 9773 8973.6 1 80.041 87.1715 < 2.2e-16 ***
## 3 9773 8981.2 0 0.000
## 4 9772 8972.1 1 9.060 9.8678 0.001687 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 3 35173.25
## 2 3a 35088.42
## 3 3b 35096.66
## 4 3c 35088.79##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (p * A + ((1 -
## p) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 0.904464 0.201785 4.482 7.47e-06 ***
## phi 0.019060 0.004299 4.434 9.35e-06 ***
## alpha 0.804226 0.028323 28.395 < 2e-16 ***
## A 2.842362 0.106915 26.585 < 2e-16 ***
## k 21.910164 2.331017 9.399 < 2e-16 ***
## p 0.211069 0.018502 11.408 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9582 on 9773 degrees of freedom
##
## Number of iterations to convergence: 5
## Achieved convergence tolerance: 1.087e-06
## (3197 observations deleted due to missingness)summary
- add p model: fits
- add s model: fits
- add s+p model: fits
plot residuals
predict and plot
## Warning: Removed 1578 rows containing missing values (geom_point).## Warning: Removed 1031 row(s) containing missing values (geom_path).
plotting 2
221 - Eastern Broadleaf Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 5424 7458.3
## 2 5423 7443.2 1 15.115 11.013 0.0009108 ***
## 3 5422 7169.6 1 273.579 206.893 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 24022.47
## 2 2 24013.46
## 3 3 23812.23##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -0.858152 0.137341 -6.248 4.46e-10 ***
## phi 0.018698 0.006456 2.896 0.00379 **
## alpha 0.711059 0.046491 15.295 < 2e-16 ***
## A 5.179889 0.188163 27.529 < 2e-16 ***
## k 15.877043 1.973121 8.047 1.04e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.15 on 5422 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 6.631e-06
## (35 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_221, :
## number of iterations exceeded maximum of 50
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_221, :
## step factor 0.000488281 reduced below 'minFactor' of 0.000976562
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (p * A + ((1 - p) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 5422 7169.6
## 2 5421 7110.0 1 59.66 45.488 1.696e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 3 23812.23
## 2 3a 23768.88
## 3 3b NA
## 4 3c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (p * A + ((1 -
## p) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -0.922607 0.133022 -6.936 4.51e-12 ***
## phi 0.019262 0.006429 2.996 0.00275 **
## alpha 0.710211 0.045749 15.524 < 2e-16 ***
## A 7.872409 1.080917 7.283 3.73e-13 ***
## k 245.381353 102.237357 2.400 0.01642 *
## p 0.362923 0.036056 10.066 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.145 on 5421 degrees of freedom
##
## Number of iterations to convergence: 19
## Achieved convergence tolerance: 9.037e-06
## (35 observations deleted due to missingness)summary
- add p model: fits
- add s model: does not fit
- add s+p model: does not fit
plot residuals
predict and plot
## Warning: Removed 16 rows containing missing values (geom_point).## Warning: Removed 1036 row(s) containing missing values (geom_path).
plotting 2
222 - Midwest Broadleaf Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 2742 3071.9
## 2 2741 3066.0 1 5.924 5.2961 0.02145 *
## 3 2740 2856.2 1 209.815 201.2809 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 11096.73
## 2 2 11093.43
## 3 3 10900.85##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -0.20088 0.27196 -0.739 0.4602
## phi 0.02711 0.01188 2.282 0.0226 *
## alpha 0.84796 0.05418 15.650 <2e-16 ***
## A 4.35985 0.24407 17.863 <2e-16 ***
## k 19.77609 2.04423 9.674 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.021 on 2740 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 8.797e-06
## (809 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_222, :
## number of iterations exceeded maximum of 50
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_222, :
## number of iterations exceeded maximum of 50
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (p * A + ((1 - p) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 2740 2856.2
## 2 2739 2784.3 1 71.864 70.695 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 3 10900.85
## 2 3a 10832.89
## 3 3b NA
## 4 3c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (p * A + ((1 -
## p) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -0.17281 0.27101 -0.638 0.523747
## phi 0.02009 0.01146 1.753 0.079635 .
## alpha 0.83616 0.05343 15.649 < 2e-16 ***
## A 6.76142 0.75296 8.980 < 2e-16 ***
## k 163.39875 43.41256 3.764 0.000171 ***
## p 0.23907 0.01971 12.127 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.008 on 2739 degrees of freedom
##
## Number of iterations to convergence: 12
## Achieved convergence tolerance: 3.685e-06
## (809 observations deleted due to missingness)summary
- add p model: fits
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.89438, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -5.4786, p-value = 4.287e-08
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 422 rows containing missing values (geom_point).## Warning: Removed 1108 row(s) containing missing values (geom_path).
plotting 2
223 - Central Interior Broadleaf Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 5267 6923.8
## 2 5266 6915.7 1 8.11 6.1755 0.01298 *
## 3 5265 6729.8 1 185.93 145.4637 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 22279.18
## 2 2 22275.00
## 3 3 22133.38##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -0.90464 0.13883 -6.516 7.88e-11 ***
## phi -0.02165 0.00849 -2.550 0.0108 *
## alpha 0.65824 0.05115 12.870 < 2e-16 ***
## A 5.65960 0.23285 24.305 < 2e-16 ***
## k 34.88900 3.58050 9.744 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.131 on 5265 degrees of freedom
##
## Number of iterations to convergence: 12
## Achieved convergence tolerance: 9.169e-06
## (1120 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_223, :
## number of iterations exceeded maximum of 50
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_223, :
## step factor 0.000488281 reduced below 'minFactor' of 0.000976562
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_223, :
## singular gradient
## model AIC
## 1 3 22133.38
## 2 3a NA
## 3 3b NA
## 4 3c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -0.90464 0.13883 -6.516 7.88e-11 ***
## phi -0.02165 0.00849 -2.550 0.0108 *
## alpha 0.65824 0.05115 12.870 < 2e-16 ***
## A 5.65960 0.23285 24.305 < 2e-16 ***
## k 34.88900 3.58050 9.744 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.131 on 5265 degrees of freedom
##
## Number of iterations to convergence: 12
## Achieved convergence tolerance: 9.169e-06
## (1120 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
predict and plot
## Warning: Removed 558 rows containing missing values (geom_point).## Warning: Removed 1002 row(s) containing missing values (geom_path).
plotting 2
231 - Southeastern Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 8057 19562
## 2 8056 19555 1 6.95 2.8618 0.09074 .
## 3 8055 17895 1 1659.77 747.0906 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 42193.22
## 2 2 42192.36
## 3 3 41479.47##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 0.814066 0.185610 4.386 1.17e-05 ***
## phi 0.003486 0.005940 0.587 0.557
## alpha 0.871461 0.028993 30.058 < 2e-16 ***
## A 4.558598 0.149590 30.474 < 2e-16 ***
## k 1.767603 0.277782 6.363 2.08e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.491 on 8055 degrees of freedom
##
## Number of iterations to convergence: 10
## Achieved convergence tolerance: 8.509e-06
## (140 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
model selection 2
## Error in numericDeriv(form[[3L]], names(ind), env, central = nDcentral) :
## Missing value or an infinity produced when evaluating the model
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (p * A + ((1 - p) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 8055 17895
## 2 8054 17815 1 80.521 36.403 1.675e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 3 41479.47
## 2 3a 41445.12
## 3 3b 41438.25
## 4 3c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (A * B_plt_t1_MgHa^s/(k^s +
## B_plt_t1_MgHa^s))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 0.712715 0.179272 3.976 7.08e-05 ***
## phi 0.003346 0.005912 0.566 0.5714
## alpha 0.870534 0.028740 30.290 < 2e-16 ***
## A 7.785773 3.245462 2.399 0.0165 *
## k 19.088949 77.814643 0.245 0.8062
## s 0.206994 0.097156 2.131 0.0332 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.487 on 8054 degrees of freedom
##
## Number of iterations to convergence: 16
## Achieved convergence tolerance: 4.67e-06
## (140 observations deleted due to missingness)summary
- add p model: fits
- add s model: fits
- add s+p model: does not fit
plot residuals
predict and plot
## Warning: Removed 72 rows containing missing values (geom_point).## Warning: Removed 1017 row(s) containing missing values (geom_path).
plotting 2
232 - Outer Coastal Plain Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 8011 21190
## 2 8010 21183 1 6.7 2.5316 0.1116
## 3 8009 19444 1 1738.8 716.1909 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 41553.79
## 2 2 41553.26
## 3 3 40868.87##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 0.680994 0.201717 3.376 0.000739 ***
## phi 0.001027 0.006109 0.168 0.866521
## alpha 0.871185 0.029193 29.842 < 2e-16 ***
## A 4.601909 0.173334 26.549 < 2e-16 ***
## k 7.180785 0.651847 11.016 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.558 on 8009 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 5.547e-06
## (180 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
model selection 2
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (p * A + ((1 - p) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)))
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (A * B_plt_t1_MgHa^s/(k^s + B_plt_t1_MgHa^s))
## Model 4: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (p * A + ((1 - p) * A * B_plt_t1_MgHa^s/(k^s + B_plt_t1_MgHa^s)))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 8009 19444
## 2 8008 19235 1 209.897 87.3873 < 2e-16 ***
## 3 8008 19239 0 0.000
## 4 8007 19231 1 8.249 3.4346 0.06388 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 3 40868.87
## 2 3a 40783.89
## 3 3b 40785.71
## 4 3c 40784.27##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (p * A + ((1 -
## p) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 0.6269578 0.1962586 3.195 0.00141 **
## phi -0.0004434 0.0060463 -0.073 0.94154
## alpha 0.8634163 0.0287793 30.001 < 2e-16 ***
## A 5.1542031 0.2207891 23.344 < 2e-16 ***
## k 32.7456324 6.0571195 5.406 6.63e-08 ***
## p 0.3644776 0.0261755 13.924 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.55 on 8008 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 4.497e-06
## (180 observations deleted due to missingness)summary
- add p model: fits
- add s model: fits
- add s+p model: fits
plot residuals
predict and plot
## Warning: Removed 87 rows containing missing values (geom_point).## Warning: Removed 931 row(s) containing missing values (geom_path).
plotting 2
234 - Lower Mississippi Riverine Forest
model selection 1
## Error in nls(fg_2, data = G_234, start = c(ge = ge.start, phi = phi.start, :
## number of iterations exceeded maximum of 50
## model AIC
## 1 1 4393.881
## 2 2 NA
## 3 3 4348.439##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 1.30681 1.22127 1.070 0.285
## phi -0.01652 0.02677 -0.617 0.537
## alpha 0.83358 0.10515 7.928 7.21e-15 ***
## A 3.81431 0.76070 5.014 6.52e-07 ***
## k 1.86697 1.30984 1.425 0.154
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.604 on 828 degrees of freedom
##
## Number of iterations to convergence: 18
## Achieved convergence tolerance: 9.459e-06
## (29 observations deleted due to missingness)summary
- simple model: fits
- phi model: does not fit
- phi-alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_234, :
## number of iterations exceeded maximum of 50
## Error in numericDeriv(form[[3L]], names(ind), env, central = nDcentral) :
## Missing value or an infinity produced when evaluating the model
## Error in numericDeriv(form[[3L]], names(ind), env, central = nDcentral) :
## Missing value or an infinity produced when evaluating the model
## model AIC
## 1 3 4348.439
## 2 3a NA
## 3 3b NA
## 4 3c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 1.30681 1.22127 1.070 0.285
## phi -0.01652 0.02677 -0.617 0.537
## alpha 0.83358 0.10515 7.928 7.21e-15 ***
## A 3.81431 0.76070 5.014 6.52e-07 ***
## k 1.86697 1.30984 1.425 0.154
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.604 on 828 degrees of freedom
##
## Number of iterations to convergence: 18
## Achieved convergence tolerance: 9.459e-06
## (29 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.90981, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -2.7773, p-value = 0.005481
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 15 rows containing missing values (geom_point).## Warning: Removed 645 row(s) containing missing values (geom_path).
plotting 2
242 - Pacific Lowland Mixed Forest
model selection 1
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## model AIC
## 1 1 NA
## 2 2 NA
## 3 3 NA## Warning in min(AIC1_242$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_242.' not foundsummary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
251 - Prairie Parkland (Temperate)
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 979 1429.8
## 2 978 1429.8 1 0.0483 0.0330 0.855861
## 3 977 1418.2 1 11.5087 7.9281 0.004965 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 4162.017
## 2 2 4163.983
## 3 3 4158.047##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 0.070204 0.602700 0.116 0.90729
## phi -0.007581 0.017898 -0.424 0.67199
## alpha 0.459818 0.155166 2.963 0.00312 **
## A 3.386890 0.428713 7.900 7.47e-15 ***
## k 12.024248 3.777479 3.183 0.00150 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.205 on 977 degrees of freedom
##
## Number of iterations to convergence: 13
## Achieved convergence tolerance: 6.046e-06
## (412 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_251, :
## step factor 0.000488281 reduced below 'minFactor' of 0.000976562
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_251, :
## step factor 0.000488281 reduced below 'minFactor' of 0.000976562
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_251, :
## step factor 0.000488281 reduced below 'minFactor' of 0.000976562
## model AIC
## 1 3 4158.047
## 2 3a NA
## 3 3b NA
## 4 3c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 0.070204 0.602700 0.116 0.90729
## phi -0.007581 0.017898 -0.424 0.67199
## alpha 0.459818 0.155166 2.963 0.00312 **
## A 3.386890 0.428713 7.900 7.47e-15 ***
## k 12.024248 3.777479 3.183 0.00150 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.205 on 977 degrees of freedom
##
## Number of iterations to convergence: 13
## Achieved convergence tolerance: 6.046e-06
## (412 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.71976, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -2.0823, p-value = 0.03732
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 220 rows containing missing values (geom_point).## Warning: Removed 1176 row(s) containing missing values (geom_path).
plotting 2
255 - Prairie Parkland (Subtropical)
model selection 1
## Error in nls(fg_1, data = G_255, start = c(ge = ge.start, A = A.start, :
## number of iterations exceeded maximum of 50
## Error in nls(fg_2, data = G_255, start = c(ge = ge.start, phi = phi.start, :
## number of iterations exceeded maximum of 50
## Error in nls(fg_3, data = G_255, start = c(ge = ge.start, phi = phi.start, :
## number of iterations exceeded maximum of 50
## model AIC
## 1 1 NA
## 2 2 NA
## 3 3 NA## Warning in min(AIC1_255$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_255.' not foundsummary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
261 - California Coastal Chaparral Forest and Shrub
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
262 - California Dry Steppe
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
263 - California Coastal Steppe - Mixed Forest and Redwood Forest
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
model selection 2
summary
add p model: does not fit
add s model: does not fit
add s+p model: does not fit
note: model fit, but fit was funky due to data being sparse
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
313 - Colorado Plateau Semi-Desert
model selection 1
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## model AIC
## 1 1 NA
## 2 2 NA
## 3 3 NA## Warning in min(AIC1_313$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_313.' not foundsummary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
315 - Southwest Plateau and Plains Dry Steppe and Shrub
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
321 - Chihuahuan Semi-Desert
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
322 - American Semidesert and Desert
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
331 - Great Plains/Palouse Dry Steppe
model selection 1
## Error in nls(fg_1, data = G_331, start = c(ge = ge.start, A = A.start, :
## number of iterations exceeded maximum of 50
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 107 383.70
## 2 106 371.31 1 12.387 3.5362 0.06279 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 NA
## 2 2 449.6049
## 3 3 447.9623##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -0.003205 7.346050 0.000 0.9997
## phi 0.360038 0.192061 1.875 0.0636 .
## alpha 0.995614 0.450664 2.209 0.0293 *
## A 0.714946 1.067535 0.670 0.5045
## k 6.442888 12.204161 0.528 0.5987
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.872 on 106 degrees of freedom
##
## Number of iterations to convergence: 21
## Achieved convergence tolerance: 9.792e-06
## (7 observations deleted due to missingness)summary
- simple model: does not fit
- phi model: fits
- phi-alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_331, :
## parameters without starting value in 'data': s
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_331, :
## parameters without starting value in 'data': s
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (p * A + ((1 - p) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 106 371.31
## 2 105 371.17 1 0.1343 0.038 0.8458
## model AIC
## 1 3 447.9623
## 2 3a 449.9222
## 3 3b NA
## 4 3c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -0.003205 7.346050 0.000 0.9997
## phi 0.360038 0.192061 1.875 0.0636 .
## alpha 0.995614 0.450664 2.209 0.0293 *
## A 0.714946 1.067535 0.670 0.5045
## k 6.442888 12.204161 0.528 0.5987
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.872 on 106 degrees of freedom
##
## Number of iterations to convergence: 21
## Achieved convergence tolerance: 9.792e-06
## (7 observations deleted due to missingness)summary
- add p model: fits
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.70407, p-value = 1.192e-13
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -0.56475, p-value = 0.5722
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 4 rows containing missing values (geom_point).## Warning: Removed 1282 row(s) containing missing values (geom_path).
plotting 2
332 - Great Plains Steppe
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 136 120.26
## 2 135 119.67 1 0.58613 0.6612 0.4176
## 3 134 117.29 1 2.38196 2.7213 0.1014
## model AIC
## 1 1 459.1608
## 2 2 460.4817
## 3 3 459.6871##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * A *
## B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 0.4799 1.7461 0.275 0.7839
## A 3.4663 1.3351 2.596 0.0105 *
## k 62.4630 26.3410 2.371 0.0191 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9403 on 136 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 8.801e-06
## (15 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
model selection 2
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (p * A + ((1 - p) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)))
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (A * B_plt_t1_MgHa^s/(k^s + B_plt_t1_MgHa^s))
## Model 4: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (p * A + ((1 - p) * A * B_plt_t1_MgHa^s/(k^s + B_plt_t1_MgHa^s)))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 136 120.26
## 2 135 117.70 1 2.5603 2.9366 0.08889 .
## 3 135 118.66 0 0.0000
## 4 134 117.39 1 1.2740 1.4542 0.22997
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 459.1608
## 2 1a 458.1696
## 3 1b 459.3057
## 4 1c 459.8054##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (p *
## A + ((1 - p) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 0.33561 1.63267 0.206 0.8374
## A 4.90376 2.82747 1.734 0.0851 .
## k 166.96848 159.88931 1.044 0.2982
## p 0.10369 0.04841 2.142 0.0340 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9337 on 135 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 3.644e-06
## (15 observations deleted due to missingness)summary
- add p model: fits
- add s model: fits
- add s+p model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.90099, p-value = 3.898e-08
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -2.0874, p-value = 0.03685
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 9 rows containing missing values (geom_point).## Warning: Removed 1140 row(s) containing missing values (geom_path).
plotting 2
341 - Intermountain Semi-desert & Desert
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
342 - Intermountain Semi-Desert
model selection 1
## Error in nls(fg_1, data = G_342, start = c(ge = ge.start, A = A.start, :
## number of iterations exceeded maximum of 50
## Error in nls(fg_2, data = G_342, start = c(ge = ge.start, phi = phi.start, :
## number of iterations exceeded maximum of 50
## Error in nls(fg_3, data = G_342, start = c(ge = ge.start, phi = phi.start, :
## number of iterations exceeded maximum of 50
## model AIC
## 1 1 NA
## 2 2 NA
## 3 3 NA## Warning in min(AIC1_342$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_342.' not foundsummary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
411 - Everglades
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M211 - Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 5087 4233.1
## 2 5086 4213.2 1 19.876 23.993 9.968e-07 ***
## 3 5085 3988.2 1 225.054 286.949 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 19222.23
## 2 2 19200.27
## 3 3 18922.85##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 0.80095 0.22370 3.580 0.000346 ***
## phi 0.01711 0.00514 3.329 0.000879 ***
## alpha 0.63264 0.03477 18.195 < 2e-16 ***
## A 2.89265 0.11960 24.186 < 2e-16 ***
## k 2.67623 0.46800 5.718 1.14e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8856 on 5085 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 3.751e-06
## (14 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
model selection 2
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (p * A + ((1 - p) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)))
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (A * B_plt_t1_MgHa^s/(k^s + B_plt_t1_MgHa^s))
## Model 4: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (p * A + ((1 - p) * A * B_plt_t1_MgHa^s/(k^s + B_plt_t1_MgHa^s)))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 5085 3988.2
## 2 5084 3985.4 1 2.7938 3.5640 0.0591 .
## 3 5084 3986.8 0 0.0000
## 4 5083 3985.1 1 1.6672 2.1265 0.1448
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 3 18922.85
## 2 3a 18921.29
## 3 3b 18923.11
## 4 3c 18922.98##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (p * A + ((1 -
## p) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 0.794538 0.223161 3.560 0.000374 ***
## phi 0.016671 0.005141 3.243 0.001192 **
## alpha 0.633054 0.034748 18.218 < 2e-16 ***
## A 2.920326 0.122450 23.849 < 2e-16 ***
## k 4.756600 1.701132 2.796 0.005191 **
## p 0.271191 0.124863 2.172 0.029909 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8854 on 5084 degrees of freedom
##
## Number of iterations to convergence: 4
## Achieved convergence tolerance: 9.458e-07
## (14 observations deleted due to missingness)summary
- add p model: fits
- add s model: fits
- add s+p model: fits
plot residuals
predict and plot
## Warning: Removed 5 rows containing missing values (geom_point).## Warning: Removed 1108 row(s) containing missing values (geom_path).
plotting 2
M221 - Eastern Broadleaf Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 5232 11548
## 2 5231 11544 1 3.721 1.6861 0.1942
## 3 5230 11293 1 251.048 116.2620 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 25880.58
## 2 2 25880.89
## 3 3 25767.79##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -0.021668 0.212436 -0.102 0.919
## phi -0.008750 0.008883 -0.985 0.325
## alpha 0.825669 0.072299 11.420 <2e-16 ***
## A 4.326398 0.198641 21.780 <2e-16 ***
## k 9.233071 2.088528 4.421 1e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.469 on 5230 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 7.067e-06
## (27 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_M221, :
## number of iterations exceeded maximum of 50
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M221, :
## step factor 0.000488281 reduced below 'minFactor' of 0.000976562
## Error in numericDeriv(form[[3L]], names(ind), env, central = nDcentral) :
## Missing value or an infinity produced when evaluating the model
## model AIC
## 1 3 25767.79
## 2 3a NA
## 3 3b NA
## 4 3c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -0.021668 0.212436 -0.102 0.919
## phi -0.008750 0.008883 -0.985 0.325
## alpha 0.825669 0.072299 11.420 <2e-16 ***
## A 4.326398 0.198641 21.780 <2e-16 ***
## k 9.233071 2.088528 4.421 1e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.469 on 5230 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 7.067e-06
## (27 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
predict and plot
## Warning: Removed 13 rows containing missing values (geom_point).## Warning: Removed 982 row(s) containing missing values (geom_path).
plotting 2
M223 - Ozark Broadleaf Forest Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 598 960.81
## 2 597 960.07 1 0.7388 0.4594 0.4982
## 3 596 931.53 1 28.5416 18.2611 2.242e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 2572.128
## 2 2 2573.666
## 3 3 2557.528##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 2.907462 1.857044 1.566 0.118
## phi 0.006323 0.033208 0.190 0.849
## alpha 0.960964 0.207502 4.631 4.47e-06 ***
## A 1.927068 0.465506 4.140 3.98e-05 ***
## k 9.122929 6.170366 1.479 0.140
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.25 on 596 degrees of freedom
##
## Number of iterations to convergence: 12
## Achieved convergence tolerance: 6.326e-06
## (3 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M223, :
## number of iterations exceeded maximum of 50
## Error in numericDeriv(form[[3L]], names(ind), env, central = nDcentral) :
## Missing value or an infinity produced when evaluating the model
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (p * A + ((1 - p) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 596 931.53
## 2 595 926.65 1 4.8783 3.1324 0.07726 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 3 2557.528
## 2 3a 2556.372
## 3 3b NA
## 4 3c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (p * A + ((1 -
## p) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 3.087e+00 1.931e+00 1.599 0.110
## phi 3.113e-03 3.267e-02 0.095 0.924
## alpha 9.711e-01 2.038e-01 4.766 2.36e-06 ***
## A 4.942e+00 1.758e+01 0.281 0.779
## k 8.449e+02 5.159e+03 0.164 0.870
## p 2.656e-01 8.955e-01 0.297 0.767
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.248 on 595 degrees of freedom
##
## Number of iterations to convergence: 14
## Achieved convergence tolerance: 6.996e-06
## (3 observations deleted due to missingness)summary
- add p model: fits
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.92974, p-value = 3.585e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -0.9579, p-value = 0.3381
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 3 rows containing missing values (geom_point).## Warning: Removed 1175 row(s) containing missing values (geom_path).
plotting 2
M231 - Ouachita Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 668 936.72
## 2 667 920.45 1 16.274 11.793 0.0006314 ***
## 3 666 885.05 1 35.402 26.640 3.24e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 2792.478
## 2 2 2782.718
## 3 3 2758.401##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 3.44313 2.33472 1.475 0.140753
## phi 0.07010 0.03005 2.333 0.019970 *
## alpha 0.88785 0.15922 5.576 3.58e-08 ***
## A 1.64621 0.47232 3.485 0.000524 ***
## k 3.70623 2.06642 1.794 0.073339 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.153 on 666 degrees of freedom
##
## Number of iterations to convergence: 15
## Achieved convergence tolerance: 5.306e-06
## (7 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_M231, :
## step factor 0.000488281 reduced below 'minFactor' of 0.000976562
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M231, :
## step factor 0.000488281 reduced below 'minFactor' of 0.000976562
## Error in numericDeriv(form[[3L]], names(ind), env, central = nDcentral) :
## Missing value or an infinity produced when evaluating the model
## model AIC
## 1 3 2758.401
## 2 3a NA
## 3 3b NA
## 4 3c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 3.44313 2.33472 1.475 0.140753
## phi 0.07010 0.03005 2.333 0.019970 *
## alpha 0.88785 0.15922 5.576 3.58e-08 ***
## A 1.64621 0.47232 3.485 0.000524 ***
## k 3.70623 2.06642 1.794 0.073339 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.153 on 666 degrees of freedom
##
## Number of iterations to convergence: 15
## Achieved convergence tolerance: 5.306e-06
## (7 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.95762, p-value = 5.457e-13
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -3.9997, p-value = 6.343e-05
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1218 row(s) containing missing values (geom_path).
plotting 2
M242 - Cascade Mixed Forest
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M261 - Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 165 194.45
## 2 164 185.13 1 9.3239 8.2598 0.00459 **
## 3 163 181.44 1 3.6902 3.3152 0.07047 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 584.8960
## 2 2 578.6409
## 3 3 577.2582##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -0.53555 4.43455 -0.121 0.904025
## phi 0.16308 0.04179 3.902 0.000139 ***
## alpha 0.71187 0.36243 1.964 0.051211 .
## A 16.71690 21.70358 0.770 0.442274
## k 413.64399 385.93166 1.072 0.285391
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.055 on 163 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 1.708e-06
## (172 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M261, :
## number of iterations exceeded maximum of 50
## Error in numericDeriv(form[[3L]], names(ind), env, central = nDcentral) :
## Missing value or an infinity produced when evaluating the model
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (p * A + ((1 - p) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 163 181.44
## 2 162 180.72 1 0.71284 0.639 0.4252
## model AIC
## 1 3 577.2582
## 2 3a 578.5969
## 3 3b NA
## 4 3c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -0.53555 4.43455 -0.121 0.904025
## phi 0.16308 0.04179 3.902 0.000139 ***
## alpha 0.71187 0.36243 1.964 0.051211 .
## A 16.71690 21.70358 0.770 0.442274
## k 413.64399 385.93166 1.072 0.285391
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.055 on 163 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 1.708e-06
## (172 observations deleted due to missingness)summary
- add p model: fits
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.96414, p-value = 0.0002509
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = 1.5878, p-value = 0.1123
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 81 rows containing missing values (geom_point).## Warning: Removed 1274 row(s) containing missing values (geom_path).
plotting 2
M262 - Califormia Coastal Range = Coniferous Forest - Open woodland Shrub Meadow
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M313 - Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow
model selection 1
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## model AIC
## 1 1 NA
## 2 2 NA
## 3 3 NA## Warning in min(AIC1_M313$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_M313.' not foundsummary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M331 - Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow
model selection 1
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## model AIC
## 1 1 NA
## 2 2 NA
## 3 3 NA## Warning in min(AIC1_M331$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_M331.' not foundsummary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M332 - Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M333 - Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M334 - Black Hills Coniferous Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 217 220.35
## 2 216 220.32 1 0.0274 0.0269 0.87
## 3 215 199.25 1 21.0776 22.7442 3.411e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 664.8357
## 2 2 666.8083
## 3 3 646.6857##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 2.86821 5.35113 0.536 0.59251
## phi -0.04321 0.04806 -0.899 0.36953
## alpha 0.90060 0.15821 5.693 4.07e-08 ***
## A 1.70528 1.17880 1.447 0.14946
## k 39.64935 14.75062 2.688 0.00775 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9627 on 215 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 9.633e-06
## (90 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
model selection 2
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (p * A + ((1 - p) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)))
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (A * B_plt_t1_MgHa^s/(k^s + B_plt_t1_MgHa^s))
## Model 4: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (p * A + ((1 - p) * A * B_plt_t1_MgHa^s/(k^s + B_plt_t1_MgHa^s)))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 215 199.25
## 2 214 199.16 1 0.09064 0.0974 0.75528
## 3 214 197.84 0 0.00000
## 4 213 194.91 1 2.93804 3.2108 0.07457 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 3 646.6857
## 2 3a 648.5856
## 3 3b 647.1336
## 4 3c 645.8421##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (p * A + ((1 -
## p) * A * B_plt_t1_MgHa^s/(k^s + B_plt_t1_MgHa^s)))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 1.95469 4.16003 0.470 0.6389
## phi -0.03914 0.04751 -0.824 0.4110
## alpha 0.91363 0.15593 5.859 1.75e-08 ***
## A 1.45952 0.86394 1.689 0.0926 .
## k 35.13504 6.47183 5.429 1.54e-07 ***
## p 0.22941 0.10064 2.279 0.0236 *
## s 3.02467 1.67678 1.804 0.0727 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9566 on 213 degrees of freedom
##
## Number of iterations to convergence: 11
## Achieved convergence tolerance: 3.834e-06
## (90 observations deleted due to missingness)summary
- add p model: fits
- add s model: fits
- add s+p model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.7338, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = 0.0049161, p-value = 0.9961
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 41 rows containing missing values (geom_point).## Warning: Removed 1264 row(s) containing missing values (geom_path).
plotting 2
M341 - Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
Fitted parameters
Best / selected models by ecoprovince
| Code | Ecoregion | Sel.Mod |
|---|---|---|
| 211 | Northeastern Mixed Forest | 3b |
| 212 | Laurentian Mixed Forest | 3a |
| 221 | Eastern Broadleaf Forest | 3a |
| 222 | Midwest Broadleaf Forest | 3a |
| 223 | Central Interior Broadleaf Forest | 3 |
| 231 | Southeastern Mixed Forest | 3b |
| 232 | Outer Coastal Plain Mixed Forest | 3a |
| 234 | Lower Mississippi Riverine Forest | 3 |
| 242 | Pacific Lowland Mixed Forest | NA |
| 251 | Prairie Parkland (Temperate) | 3 |
| 255 | Prairie Parkland (Subtropical) | NA |
| 261 | California Coastal Chaparral Forest and Shrub | NA |
| 262 | California Dry Steppe | NA |
| 263 | California Coastal Steppe - Mixed Forest and Redwood Forest | NA |
| 313 | Colorado Plateau Semi-Desert | NA |
| 315 | Southwest Plateau and Plains Dry Steppe and Shrub | NA |
| 321 | Chihuahuan Semi-Desert | NA |
| 322 | American Semidesert and Desert | NA |
| 331 | Great Plains/Palouse Dry Steppe | 3 |
| 332 | Great Plains Steppe | 1a |
| 341 | Intermountain Semi-Desert and Desert | NA |
| 342 | Intermountain Semi-Desert | NA |
| 411 | Everglades | NA |
| M211 | Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow | 3a |
| M221 | Central Appalachian Broadleaf Forest - Coniferous Forest - Meadow | 3 |
| M223 | Ozark Broadleaf Forest Meadow | 3a |
| M231 | Ouachita Mixed Forest | 3 |
| M242 | Cascade Mixed Forest | NA |
| M261 | Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow | 3 |
| M262 | California Coastal Range Coniferous Forest - Open Woodland - Shrub - Meadow | NA |
| M313 | Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow | NA |
| M331 | Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow | NA |
| M332 | Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | NA |
| M333 | Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | NA |
| M334 | Black Hills Coniferous Forest | 3c |
| M341 | Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow | NA |
table by ecoprovince
| Code | Ecoregion | region | n.obs | n.plots | ge | ge.variance | ge.2.5 | ge.97.5 | phi | phi.variance | phi.2.5 | phi.97.5 | alpha | alpha.variance | alpha.2.5 | alpha.97.5 | A | A.2.5 | A.97.5 | k | k.2.5 | k.97.5 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 211 | Northeastern Mixed Forest | east | 4834 | 2417 | 0.0219361 | 0.0306593 | -0.3213364 | 0.3652087 | 0.0085678 | 0.0000320 | -0.0025282 | 0.0196638 | 0.6266651 | 0.0016686 | 0.5465834 | 0.7067468 | 4.0600295 | 3.5096040 | 4.610455 | 7.729586 | 4.6937264 | 10.765446 |
| 212 | Laurentian Mixed Forest | east | 12976 | 6488 | 0.9044638 | 0.0407172 | 0.5089233 | 1.3000042 | 0.0190601 | 0.0000185 | 0.0106337 | 0.0274864 | 0.8042261 | 0.0008022 | 0.7487076 | 0.8597447 | 2.8423616 | 2.6327869 | 3.051936 | 21.910164 | 17.3408884 | 26.479440 |
| 221 | Eastern Broadleaf Forest | east | 5462 | 2731 | -0.9226068 | 0.0176949 | -1.1833838 | -0.6618298 | 0.0192624 | 0.0000413 | 0.0066591 | 0.0318656 | 0.7102109 | 0.0020930 | 0.6205242 | 0.7998976 | 7.8724086 | 5.7533770 | 9.991440 | 245.381353 | 44.9550652 | 445.807640 |
| 222 | Midwest Broadleaf Forest | east | 3554 | 1777 | -0.1728129 | 0.0734466 | -0.7042182 | 0.3585924 | 0.0200879 | 0.0001312 | -0.0023757 | 0.0425515 | 0.8361600 | 0.0028550 | 0.7313887 | 0.9409313 | 6.7614239 | 5.2849897 | 8.237858 | 163.398751 | 78.2740742 | 248.523429 |
| 223 | Central Interior Broadleaf Forest | east | 6390 | 3195 | -0.9046411 | 0.0192728 | -1.1767988 | -0.6324833 | -0.0216540 | 0.0000721 | -0.0382987 | -0.0050092 | 0.6582379 | 0.0026159 | 0.5579699 | 0.7585059 | 5.6595971 | 5.2031064 | 6.116088 | 34.888998 | 27.8697324 | 41.908263 |
| 231 | Southeastern Mixed Forest | east | 8200 | 4100 | 0.7127154 | 0.0321384 | 0.3612961 | 1.0641348 | 0.0033457 | 0.0000349 | -0.0082427 | 0.0149341 | 0.8705342 | 0.0008260 | 0.8141957 | 0.9268727 | 7.7857734 | 1.4238278 | 14.147719 | 19.088949 | -133.4478718 | 171.625770 |
| 232 | Outer Coastal Plain Mixed Forest | east | 8194 | 4097 | 0.6269578 | 0.0385174 | 0.2422398 | 1.0116757 | -0.0004434 | 0.0000366 | -0.0122957 | 0.0114089 | 0.8634163 | 0.0008283 | 0.8070013 | 0.9198313 | 5.1542031 | 4.7213990 | 5.587007 | 32.745632 | 20.8721017 | 44.619163 |
| 234 | Lower Mississippi Riverine Forest | east | 862 | 431 | 1.3068089 | 1.4915046 | -1.0903437 | 3.7039615 | -0.0165220 | 0.0007167 | -0.0690687 | 0.0360248 | 0.8335760 | 0.0110563 | 0.6271865 | 1.0399655 | 3.8143143 | 2.3211796 | 5.307449 | 1.866965 | -0.7040329 | 4.437963 |
| 242 | Pacific Lowland Mixed Forest | pacific | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 251 | Prairie Parkland (Temperate) | east | 1394 | 697 | 0.0702036 | 0.3632469 | -1.1125312 | 1.2529385 | -0.0075807 | 0.0003203 | -0.0427043 | 0.0275428 | 0.4598175 | 0.0240765 | 0.1553206 | 0.7643145 | 3.3868899 | 2.5455865 | 4.228193 | 12.024248 | 4.6113426 | 19.437154 |
| 255 | Prairie Parkland (Subtropical) | east | 446 | 223 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 261 | California Coastal Chaparral Forest and Shrub | pacific | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 262 | California Dry Steppe | pacific | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 263 | California Coastal Steppe - Mixed Forest and Redwood Forest | pacific | 4 | 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 313 | Colorado Plateau Semi-Desert | interior west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 315 | Southwest Plateau and Plains Dry Steppe and Shrub | interior west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 321 | Chihuahuan Semi-Desert | interior west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 322 | American Semidesert and Desert | interior west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 331 | Great Plains/Palouse Dry Steppe | interior west | 118 | 59 | -0.0032054 | 53.9644522 | -14.5674642 | 14.5610534 | 0.3600378 | 0.0368875 | -0.0207424 | 0.7408180 | 0.9956138 | 0.2030977 | 0.1021294 | 1.8890982 | 0.7149459 | -1.4015470 | 2.831439 | 6.442888 | -17.7530487 | 30.638824 |
| 332 | Great Plains Steppe | interior west | 154 | 77 | 0.3356066 | 2.6656092 | -2.8933109 | 3.5645242 | NA | NA | NA | NA | NA | NA | NA | NA | 4.9037624 | -0.6881114 | 10.495636 | 166.968483 | -149.2433635 | 483.180329 |
| 341 | Intermountain Semi-Desert and Desert | interior west | 4 | 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 342 | Intermountain Semi-Desert | interior west | 2 | 1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 411 | Everglades | east | 66 | 33 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M211 | Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow | east | 5104 | 2552 | 0.7945375 | 0.0498009 | 0.3570456 | 1.2320294 | 0.0166711 | 0.0000264 | 0.0065922 | 0.0267501 | 0.6330542 | 0.0012074 | 0.5649327 | 0.7011756 | 2.9203257 | 2.6802712 | 3.160380 | 4.756600 | 1.4216476 | 8.091552 |
| M221 | Central Appalachian Broadleaf Forest - Coniferous Forest - Meadow | east | 5262 | 2631 | -0.0216677 | 0.0451289 | -0.4381302 | 0.3947949 | -0.0087496 | 0.0000789 | -0.0261642 | 0.0086650 | 0.8256690 | 0.0052271 | 0.6839333 | 0.9674046 | 4.3263978 | 3.9369778 | 4.715818 | 9.233071 | 5.1386843 | 13.327457 |
| M223 | Ozark Broadleaf Forest Meadow | east | 604 | 302 | 3.0869142 | 3.7275330 | -0.7048661 | 6.8786944 | 0.0031133 | 0.0010675 | -0.0610551 | 0.0672817 | 0.9711097 | 0.0415158 | 0.5709447 | 1.3712748 | 4.9423202 | -29.5874030 | 39.472043 | 844.913093 | -9286.7227176 | 10976.548904 |
| M231 | Ouachita Mixed Forest | east | 678 | 339 | 3.4431258 | 5.4509268 | -1.1411763 | 8.0274279 | 0.0700954 | 0.0009031 | 0.0110886 | 0.1291022 | 0.8878522 | 0.0253518 | 0.5752136 | 1.2004909 | 1.6462128 | 0.7187955 | 2.573630 | 3.706232 | -0.3512556 | 7.763719 |
| M242 | Cascade Mixed Forest | pacific | 34 | 17 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M261 | Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow | pacific | 340 | 170 | -0.5355458 | 19.6652227 | -9.2921150 | 8.2210234 | 0.1630850 | 0.0017465 | 0.0805634 | 0.2456065 | 0.7118723 | 0.1313551 | -0.0037899 | 1.4275345 | 16.7169000 | -26.1395256 | 59.573326 | 413.643995 | -348.4261661 | 1175.714155 |
| M262 | California Coastal Range Coniferous Forest - Open Woodland - Shrub - Meadow | interior west | 8 | 4 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M313 | Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow | interior west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M331 | Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow | interior west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M332 | Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | interior west | 20 | 10 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M333 | Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | interior west | 22 | 11 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M334 | Black Hills Coniferous Forest | interior west | 310 | 155 | 1.9546861 | 17.3058093 | -6.2454053 | 10.1547774 | -0.0391386 | 0.0022576 | -0.1327974 | 0.0545202 | 0.9136298 | 0.0243144 | 0.6062644 | 1.2209951 | 1.4595174 | -0.2434567 | 3.162492 | 35.135035 | 22.3779877 | 47.892083 |
| M341 | Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow | interior west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
plot ge
map
## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\hogan.jaaron\Dropbox\FIA_R\Mapping\S_USA.EcoMapProvinces\S_USA.EcoMapProvinces.shp", layer: "S_USA.EcoMapProvinces"
## with 37 features
## It has 17 fields
## Integer64 fields read as strings: PROVINCE_ PROVINCE_I## Warning: package 'ggnewscale' was built under R version 4.2.1## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family not
## found in Windows font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family not
## found in Windows font database## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
plot phi (effect of DeltaPDSI)
plot alpha (biomass growth compensation effect)
plot A (asymptote of forest biomass growth in Mg/ha/yr)
## Warning: Removed 19 rows containing missing values (geom_point).
plot k (stand biomass at half biomss G in Mg/ha)
## Warning: Removed 19 rows containing missing values (geom_point).
Caclulations - weighted averages
ge (stand biomass growth enhancement factor in % 2000-2021)
## region weighted.ge weighted.ge.std_Error 95 % CI, upper
## 1 entire US 0.326397599 0.08068344 0.48453714
## 2 pacific -0.002799508 0.02318112 0.04263549
## 3 east 0.319091977 0.07339462 0.46294543
## 4 interior west 0.010105130 0.02420088 0.05753885
## 95 % CI, lower
## 1 0.16825806
## 2 -0.04823451
## 3 0.17523852
## 4 -0.03732859phi (effect of DeltaPDSI)
## region weighted.phi weighted.phi.std_Error 95 % CI, upper
## 1 entire US 0.0076907481 0.0021886250 0.011980453
## 2 pacific 0.0008525089 0.0002184578 0.001280686
## 3 east 0.0063715946 0.0021376764 0.010561440
## 4 interior west 0.0004666446 0.0004155660 0.001281154
## 95 % CI, lower
## 1 0.0034010431
## 2 0.0004243315
## 3 0.0021817489
## 4 -0.0003478647alpha (biomass growth compensation effect)
## region weighted.alpha weighted.alpha.std_Error 95 % CI, upper
## 1 entire US 0.761073471 0.013481268 0.787496757
## 2 pacific 0.003721235 0.001894561 0.007434575
## 3 east 0.751191484 0.013301670 0.777262757
## 4 interior west 0.006160752 0.001104898 0.008326353
## 95 % CI, lower
## 1 7.346502e-01
## 2 7.895873e-06
## 3 7.251202e-01
## 4 3.995151e-03A (asymptote of forest biomass growth in Mg/ha/yr)
## region weighted.A
## 1 entire US 4.958899
## 2 pacific 15.036365
## 3 east 4.928638
## 4 interior west 2.025068K (stand biomass at half biomass G in Mg/ha)
## region weighted.k
## 1 entire US 56.46396
## 2 pacific 372.06074
## 3 east 54.57977
## 4 interior west 58.56625Analysis 2: Temporally-balanced, No-harvest
211 - Northeastern Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 3724 3417.2
## 2 3723 3415.4 1 1.7416 1.8984 0.1683
## 3 3722 3389.8 1 25.6472 28.1609 1.182e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 14508.81
## 2 2 14508.91
## 3 3 14482.82##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -0.061176 0.195351 -0.313 0.754
## phi 0.008549 0.006482 1.319 0.187
## alpha 0.535540 0.096853 5.529 3.43e-08 ***
## A 3.611084 0.145354 24.843 < 2e-16 ***
## k 7.244637 0.861095 8.413 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9543 on 3722 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 1.2e-06
## (33 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
model selection 2
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (p * A + ((1 - p) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)))
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (A * B_plt_t1_MgHa^s/(k^s + B_plt_t1_MgHa^s))
## Model 4: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (p * A + ((1 - p) * A * B_plt_t1_MgHa^s/(k^s + B_plt_t1_MgHa^s)))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 3722 3389.8
## 2 3721 3386.8 1 2.96148 3.2537 0.07134 .
## 3 3721 3386.4 0 0.00000
## 4 3720 3386.3 1 0.02915 0.0320 0.85799
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 3 14482.82
## 2 3a 14481.56
## 3 3b 14481.06
## 4 3c 14483.03##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (A * B_plt_t1_MgHa^s/(k^s +
## B_plt_t1_MgHa^s))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -0.070178 0.194544 -0.361 0.718
## phi 0.008132 0.006476 1.256 0.209
## alpha 0.523308 0.097010 5.394 7.31e-08 ***
## A 3.856032 0.234882 16.417 < 2e-16 ***
## k 7.213084 1.172636 6.151 8.50e-10 ***
## s 0.728282 0.126280 5.767 8.72e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.954 on 3721 degrees of freedom
##
## Number of iterations to convergence: 5
## Achieved convergence tolerance: 3.466e-06
## (33 observations deleted due to missingness)summary
- add p model: fits
- add s model: fits
- add s+p model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.91809, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -7.9894, p-value = 1.356e-15
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 17 rows containing missing values (geom_point).## Warning: Removed 1050 row(s) containing missing values (geom_path).
plotting 2
212 - Laurentian Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 7890 7621.3
## 2 7888 7609.9 2 11.427 5.9224 0.002691 **
## 3 7887 7515.5 1 94.440 99.1083 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 28703.69
## 2 2 28691.08
## 3 3 28594.53##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 0.661266 0.210736 3.138 0.001708 **
## phi 0.017165 0.004916 3.492 0.000483 ***
## alpha 0.604017 0.057787 10.452 < 2e-16 ***
## A 2.776878 0.109209 25.427 < 2e-16 ***
## k 12.517190 0.773306 16.187 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9762 on 7887 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 3.292e-06
## (2590 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
model selection 2
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (p * A + ((1 - p) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)))
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (A * B_plt_t1_MgHa^s/(k^s + B_plt_t1_MgHa^s))
## Model 4: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (p * A + ((1 - p) * A * B_plt_t1_MgHa^s/(k^s + B_plt_t1_MgHa^s)))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 7887 7515.5
## 2 7886 7446.3 1 69.176 73.261 < 2.2e-16 ***
## 3 7886 7458.6 0 0.000
## 4 7885 7446.2 1 12.393 13.123 0.0002935 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 3 28594.53
## 2 3a 28523.55
## 3 3b 28536.56
## 4 3c 28525.43##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (p * A + ((1 -
## p) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 0.646031 0.208501 3.098 0.001952 **
## phi 0.017151 0.004879 3.516 0.000441 ***
## alpha 0.574743 0.057239 10.041 < 2e-16 ***
## A 2.983290 0.124616 23.940 < 2e-16 ***
## k 25.199015 2.781625 9.059 < 2e-16 ***
## p 0.184660 0.018056 10.227 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9717 on 7886 degrees of freedom
##
## Number of iterations to convergence: 4
## Achieved convergence tolerance: 5.487e-06
## (2590 observations deleted due to missingness)summary
- add p model: fits
- add s model: fits
- add s+p model: fits
plot residuals
predict and plot
## Warning: Removed 1290 rows containing missing values (geom_point).## Warning: Removed 1031 row(s) containing missing values (geom_path).
plotting 2
221 - Eastern Broadleaf Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 4441 6146.1
## 2 4440 6136.9 1 9.215 6.6671 0.009853 **
## 3 4439 6077.3 1 59.583 43.5207 4.687e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 19769.06
## 2 2 19764.39
## 3 3 19723.04##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -0.932344 0.148924 -6.261 4.20e-10 ***
## phi 0.017404 0.007245 2.402 0.0163 *
## alpha 0.597913 0.087478 6.835 9.31e-12 ***
## A 5.282777 0.215420 24.523 < 2e-16 ***
## k 17.411747 2.294852 7.587 3.96e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.17 on 4439 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 3.699e-06
## (32 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_221, :
## number of iterations exceeded maximum of 50
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_221, :
## step factor 0.000488281 reduced below 'minFactor' of 0.000976562
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (p * A + ((1 - p) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 4439 6077.3
## 2 4438 6034.4 1 42.924 31.568 2.043e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 3 19723.04
## 2 3a 19693.54
## 3 3b NA
## 4 3c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (p * A + ((1 -
## p) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -0.999615 0.144158 -6.934 4.68e-12 ***
## phi 0.018283 0.007219 2.533 0.0114 *
## alpha 0.586230 0.086062 6.812 1.09e-11 ***
## A 7.616262 0.951330 8.006 1.50e-15 ***
## k 199.643588 80.493759 2.480 0.0132 *
## p 0.355542 0.031621 11.244 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.166 on 4438 degrees of freedom
##
## Number of iterations to convergence: 21
## Achieved convergence tolerance: 6.352e-06
## (32 observations deleted due to missingness)summary
- add p model: fits
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.87687, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -11.809, p-value < 2.2e-16
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 16 rows containing missing values (geom_point).## Warning: Removed 1036 row(s) containing missing values (geom_path).
plotting 2
222 - Midwest Broadleaf Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 2144 2308.5
## 2 2143 2307.0 1 1.544 1.4338 0.2313
## 3 2142 2242.6 1 64.325 61.4388 7.137e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 8572.838
## 2 2 8573.402
## 3 3 8514.686##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -0.32181 0.29422 -1.094 0.274
## phi 0.02115 0.01326 1.595 0.111
## alpha 0.77021 0.09118 8.447 <2e-16 ***
## A 4.43549 0.27650 16.042 <2e-16 ***
## k 20.77089 2.32347 8.940 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.023 on 2142 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 3.419e-06
## (651 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_222, :
## number of iterations exceeded maximum of 50
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_222, :
## number of iterations exceeded maximum of 50
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (p * A + ((1 - p) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 2142 2242.6
## 2 2141 2193.0 1 49.663 48.486 4.407e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 3 8514.686
## 2 3a 8468.606
## 3 3b NA
## 4 3c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (p * A + ((1 -
## p) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -0.33719 0.28878 -1.168 0.243088
## phi 0.01668 0.01292 1.291 0.196742
## alpha 0.73132 0.08996 8.129 7.22e-16 ***
## A 6.15292 0.62215 9.890 < 2e-16 ***
## k 112.11832 28.80770 3.892 0.000102 ***
## p 0.22900 0.01975 11.592 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.012 on 2141 degrees of freedom
##
## Number of iterations to convergence: 11
## Achieved convergence tolerance: 4.252e-06
## (651 observations deleted due to missingness)summary
- add p model: fits
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.90538, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -5.8645, p-value = 4.505e-09
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 337 rows containing missing values (geom_point).## Warning: Removed 1108 row(s) containing missing values (geom_path).
plotting 2
223 - Central Interior Broadleaf Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 4276 5695.6
## 2 4275 5689.5 1 6.016 4.5199 0.03356 *
## 3 4274 5652.3 1 37.236 28.1558 1.176e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 18182.73
## 2 2 18180.21
## 3 3 18154.12##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -1.027146 0.148119 -6.935 4.69e-12 ***
## phi -0.018540 0.009594 -1.932 0.0534 .
## alpha 0.534425 0.097121 5.503 3.96e-08 ***
## A 5.643824 0.256697 21.986 < 2e-16 ***
## k 33.306989 3.856155 8.637 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.15 on 4274 degrees of freedom
##
## Number of iterations to convergence: 13
## Achieved convergence tolerance: 7.797e-06
## (843 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_223, :
## number of iterations exceeded maximum of 50
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_223, :
## step factor 0.000488281 reduced below 'minFactor' of 0.000976562
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_223, :
## singular gradient
## model AIC
## 1 3 18154.12
## 2 3a NA
## 3 3b NA
## 4 3c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -1.027146 0.148119 -6.935 4.69e-12 ***
## phi -0.018540 0.009594 -1.932 0.0534 .
## alpha 0.534425 0.097121 5.503 3.96e-08 ***
## A 5.643824 0.256697 21.986 < 2e-16 ***
## k 33.306989 3.856155 8.637 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.15 on 4274 degrees of freedom
##
## Number of iterations to convergence: 13
## Achieved convergence tolerance: 7.797e-06
## (843 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
predict and plot
## Warning: Removed 425 rows containing missing values (geom_point).## Warning: Removed 1002 row(s) containing missing values (geom_path).
plotting 2
231 - Southeastern Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 6314 14258
## 2 6313 14257 1 1.161 0.5141 0.4734
## 3 6312 14074 1 182.371 81.7891 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 32889.13
## 2 2 32890.61
## 3 3 32811.28##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 0.710232 0.203322 3.493 0.000481 ***
## phi 0.004263 0.006685 0.638 0.523684
## alpha 0.729345 0.076921 9.482 < 2e-16 ***
## A 4.570140 0.167694 27.253 < 2e-16 ***
## k 2.163263 0.355943 6.078 1.29e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.493 on 6312 degrees of freedom
##
## Number of iterations to convergence: 9
## Achieved convergence tolerance: 9.932e-06
## (127 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
model selection 2
## Error in numericDeriv(form[[3L]], names(ind), env, central = nDcentral) :
## Missing value or an infinity produced when evaluating the model
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (p * A + ((1 - p) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 6312 14074
## 2 6311 14015 1 59.663 26.867 2.247e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 3 32811.28
## 2 3a 32786.45
## 3 3b 32778.17
## 4 3c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (A * B_plt_t1_MgHa^s/(k^s +
## B_plt_t1_MgHa^s))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 5.717e-01 1.940e-01 2.947 0.00322 **
## phi 4.120e-03 6.652e-03 0.619 0.53574
## alpha 7.208e-01 7.617e-02 9.463 < 2e-16 ***
## A 8.132e+00 3.692e+00 2.203 0.02766 *
## k 3.225e+01 1.361e+02 0.237 0.81269
## s 2.182e-01 1.010e-01 2.160 0.03082 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.489 on 6311 degrees of freedom
##
## Number of iterations to convergence: 12
## Achieved convergence tolerance: 7.27e-06
## (127 observations deleted due to missingness)summary
- add p model: fits
- add s model: fits
- add s+p model: does not fit
plot residuals
predict and plot
## Warning: Removed 56 rows containing missing values (geom_point).## Warning: Removed 1017 row(s) containing missing values (geom_path).
plotting 2
232 - Outer Coastal Plain Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 6387 15897
## 2 6386 15897 1 0.114 0.0458 0.8306
## 3 6385 15679 1 217.981 88.7711 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 33025.57
## 2 2 33027.52
## 3 3 32941.30##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 0.567799 0.222585 2.551 0.0108 *
## phi -0.001798 0.006881 -0.261 0.7938
## alpha 0.671218 0.067521 9.941 <2e-16 ***
## A 4.670149 0.200119 23.337 <2e-16 ***
## k 8.928238 0.844911 10.567 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.567 on 6385 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 3.625e-06
## (150 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
model selection 2
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (p * A + ((1 - p) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)))
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (A * B_plt_t1_MgHa^s/(k^s + B_plt_t1_MgHa^s))
## Model 4: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (p * A + ((1 - p) * A * B_plt_t1_MgHa^s/(k^s + B_plt_t1_MgHa^s)))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 6385 15679
## 2 6384 15485 1 193.852 79.9204 <2e-16 ***
## 3 6384 15487 0 0.000
## 4 6383 15482 1 5.653 2.3307 0.1269
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 3 32941.30
## 2 3a 32863.80
## 3 3b 32864.87
## 4 3c 32864.54##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (p * A + ((1 -
## p) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 0.499390 0.215028 2.322 0.0202 *
## phi -0.002996 0.006803 -0.440 0.6596
## alpha 0.646923 0.065754 9.839 < 2e-16 ***
## A 5.337681 0.266692 20.014 < 2e-16 ***
## k 40.924127 8.094591 5.056 4.41e-07 ***
## p 0.346177 0.025730 13.454 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.557 on 6384 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 2.03e-06
## (150 observations deleted due to missingness)summary
- add p model: fits
- add s model: fits
- add s+p model: fits
plot residuals
predict and plot
## Warning: Removed 85 rows containing missing values (geom_point).## Warning: Removed 931 row(s) containing missing values (geom_path).
plotting 2
234 - Lower Mississippi Riverine Forest
model selection 1
## Error in nls(fg_3, data = G_234, start = c(ge = ge.start, phi = phi.start, :
## step factor 0.000488281 reduced below 'minFactor' of 0.000976562
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 684 1785.1
## 2 683 1785.0 1 0.09 0.0344 0.8529
## 3 828 2130.3 -145 -345.34 0.9113 0.7525
## model AIC
## 1 1 3613.269
## 2 2 3615.235
## 3 3 4348.439##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * A *
## B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 0.30861 0.75079 0.411 0.681
## A 3.98414 0.61078 6.523 1.34e-10 ***
## k 0.08395 0.22172 0.379 0.705
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.615 on 684 degrees of freedom
##
## Number of iterations to convergence: 31
## Achieved convergence tolerance: 6.138e-06
## (27 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_234, :
## step factor 0.000488281 reduced below 'minFactor' of 0.000976562
## Error in numericDeriv(form[[3L]], names(ind), env, central = nDcentral) :
## Missing value or an infinity produced when evaluating the model
## Error in numericDeriv(form[[3L]], names(ind), env, central = nDcentral) :
## Missing value or an infinity produced when evaluating the model
## model AIC
## 1 1 3613.269
## 2 1a NA
## 3 1b NA
## 4 1c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * A *
## B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 0.30861 0.75079 0.411 0.681
## A 3.98414 0.61078 6.523 1.34e-10 ***
## k 0.08395 0.22172 0.379 0.705
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.615 on 684 degrees of freedom
##
## Number of iterations to convergence: 31
## Achieved convergence tolerance: 6.138e-06
## (27 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.93351, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -2.4081, p-value = 0.01604
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 10 rows containing missing values (geom_point).## Warning: Removed 645 row(s) containing missing values (geom_path).
plotting 2
242 - Pacific Lowland Mixed Forest
model selection 1
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## model AIC
## 1 1 NA
## 2 2 NA
## 3 3 NA## Warning in min(AIC1_242$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_242.' not foundsummary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
251 - Prairie Parkland (Temperate)
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 868 1343.8
## 2 867 1343.6 1 0.1880 0.1213 0.7277
## 3 866 1338.4 1 5.2212 3.3783 0.0664 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 3738.098
## 2 2 3739.976
## 3 3 3738.585##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * A *
## B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -0.2616 0.5324 -0.491 0.62333
## A 3.4268 0.4412 7.767 2.27e-14 ***
## k 12.2112 4.1347 2.953 0.00323 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.244 on 868 degrees of freedom
##
## Number of iterations to convergence: 12
## Achieved convergence tolerance: 7.062e-06
## (349 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_251, :
## step factor 0.000488281 reduced below 'minFactor' of 0.000976562
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_251, :
## step factor 0.000488281 reduced below 'minFactor' of 0.000976562
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_251, :
## step factor 0.000488281 reduced below 'minFactor' of 0.000976562
## model AIC
## 1 1 3738.098
## 2 1a NA
## 3 1b NA
## 4 1c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * A *
## B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -0.2616 0.5324 -0.491 0.62333
## A 3.4268 0.4412 7.767 2.27e-14 ***
## k 12.2112 4.1347 2.953 0.00323 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.244 on 868 degrees of freedom
##
## Number of iterations to convergence: 12
## Achieved convergence tolerance: 7.062e-06
## (349 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.70974, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -3.09, p-value = 0.002002
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 172 rows containing missing values (geom_point).## Warning: Removed 1176 row(s) containing missing values (geom_path).
plotting 2
255 - Prairie Parkland (Subtropical)
model selection 1
## Error in nls(fg_1, data = G_255, start = c(ge = ge.start, A = A.start, :
## number of iterations exceeded maximum of 50
## Error in nls(fg_2, data = G_255, start = c(ge = ge.start, phi = phi.start, :
## number of iterations exceeded maximum of 50
## Error in nls(fg_3, data = G_255, start = c(ge = ge.start, phi = phi.start, :
## number of iterations exceeded maximum of 50
## model AIC
## 1 1 NA
## 2 2 NA
## 3 3 NA## Warning in min(AIC1_255$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_255.' not foundsummary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
261 - California Coastal Chaparral Forest and Shrub
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
262 - California Dry Steppe
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
263 - California Coastal Steppe - Mixed Forest and Redwood Forest
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
model selection 2
summary
add p model: does not fit
add s model: does not fit
add s+p model: does not fit
note: model fit, but fit was funky due to data being sparse
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
313 - Colorado Plateau Semi-Desert
model selection 1
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## model AIC
## 1 1 NA
## 2 2 NA
## 3 3 NA## Warning in min(AIC1_313$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_313.' not foundsummary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
315 - Southwest Plateau and Plains Dry Steppe and Shrub
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
321 - Chihuahuan Semi-Desert
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
322 - American Semidesert and Desert
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
331 - Great Plains/Palouse Dry Steppe
model selection 1
## Error in nls(fg_1, data = G_331, start = c(ge = ge.start, A = A.start, :
## number of iterations exceeded maximum of 50
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 103 336.76
## 2 102 329.06 1 7.6986 2.3864 0.1255
## model AIC
## 1 1 NA
## 2 2 428.6806
## 3 3 428.2061##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 0.8754 10.2104 0.086 0.9318
## phi 0.2478 0.1634 1.516 0.1325
## alpha 1.0137 0.5820 1.742 0.0846 .
## A 0.7672 1.3237 0.580 0.5635
## k 8.1068 12.9587 0.626 0.5330
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.796 on 102 degrees of freedom
##
## Number of iterations to convergence: 16
## Achieved convergence tolerance: 5.172e-06
## (7 observations deleted due to missingness)summary
- simple model: does not fit
- phi model: fits
- phi-alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_331, :
## parameters without starting value in 'data': s
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_331, :
## parameters without starting value in 'data': s
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (p * A + ((1 - p) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 102 329.06
## 2 101 328.98 1 0.086442 0.0265 0.8709
## model AIC
## 1 3 428.2061
## 2 3a 430.1780
## 3 3b NA
## 4 3c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 0.8754 10.2104 0.086 0.9318
## phi 0.2478 0.1634 1.516 0.1325
## alpha 1.0137 0.5820 1.742 0.0846 .
## A 0.7672 1.3237 0.580 0.5635
## k 8.1068 12.9587 0.626 0.5330
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.796 on 102 degrees of freedom
##
## Number of iterations to convergence: 16
## Achieved convergence tolerance: 5.172e-06
## (7 observations deleted due to missingness)summary
- add p model: fits
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.71219, p-value = 3.482e-13
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -0.28711, p-value = 0.774
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 4 rows containing missing values (geom_point).## Warning: Removed 1282 row(s) containing missing values (geom_path).
plotting 2
332 - Great Plains Steppe
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 128 101.432
## 2 127 100.933 1 0.49846 0.6272 0.4299
## 3 126 98.809 1 2.12414 2.7087 0.1023
## model AIC
## 1 1 420.8587
## 2 2 422.2134
## 3 3 421.4270##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * A *
## B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -0.0774 1.3362 -0.058 0.95390
## A 4.0399 1.3733 2.942 0.00388 **
## k 62.1240 24.4299 2.543 0.01218 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8902 on 128 degrees of freedom
##
## Number of iterations to convergence: 6
## Achieved convergence tolerance: 1.433e-06
## (15 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_332, :
## number of iterations exceeded maximum of 50
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (p * A + ((1 - p) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)))
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (A * B_plt_t1_MgHa^s/(k^s + B_plt_t1_MgHa^s))
## Model 4: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (p * A + ((1 - p) * A * B_plt_t1_MgHa^s/(k^s + B_plt_t1_MgHa^s)))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 128 101.432
## 2 127 99.482 1 1.9499 2.4892 0.117115
## 3 127 100.495 0 0.0000
## 4 134 117.390 -7 -16.8947 3.0501 0.005325 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 420.8587
## 2 1a 420.3159
## 3 1b 421.6435
## 4 1c 459.8054##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (p *
## A + ((1 - p) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -0.15678 1.28061 -0.122 0.9028
## A 5.23502 2.51910 2.078 0.0397 *
## k 137.15133 114.31833 1.200 0.2325
## p 0.09601 0.04778 2.010 0.0466 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8851 on 127 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 7.015e-06
## (15 observations deleted due to missingness)summary
- add p model: fits
- add s model: fits
- add s+p model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.89534, p-value = 4.022e-08
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -1.8143, p-value = 0.06963
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 8 rows containing missing values (geom_point).## Warning: Removed 1140 row(s) containing missing values (geom_path).
plotting 2
341 - Intermountain Semi-desert & Desert
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
342 - Intermountain Semi-Desert
model selection 1
## Error in nls(fg_1, data = G_342, start = c(ge = ge.start, A = A.start, :
## number of iterations exceeded maximum of 50
## Error in nls(fg_2, data = G_342, start = c(ge = ge.start, phi = phi.start, :
## number of iterations exceeded maximum of 50
## Error in nls(fg_3, data = G_342, start = c(ge = ge.start, phi = phi.start, :
## number of iterations exceeded maximum of 50
## model AIC
## 1 1 NA
## 2 2 NA
## 3 3 NA## Warning in min(AIC1_342$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_342.' not foundsummary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
411 - Everglades
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M211 - Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 3950 3001.9
## 2 3949 2994.8 1 7.1265 9.3972 0.002188 **
## 3 3948 2977.3 1 17.4463 23.1343 1.567e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 14658.1
## 2 2 14650.7
## 3 3 14629.6##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 0.913698 0.261840 3.490 0.000489 ***
## phi 0.015744 0.005705 2.760 0.005810 **
## alpha 0.445253 0.089362 4.983 6.54e-07 ***
## A 2.761313 0.132765 20.799 < 2e-16 ***
## k 2.243935 0.460393 4.874 1.14e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8684 on 3948 degrees of freedom
##
## Number of iterations to convergence: 8
## Achieved convergence tolerance: 2.208e-06
## (13 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
model selection 2
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (p * A + ((1 - p) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)))
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (A * B_plt_t1_MgHa^s/(k^s + B_plt_t1_MgHa^s))
## Model 4: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (p * A + ((1 - p) * A * B_plt_t1_MgHa^s/(k^s + B_plt_t1_MgHa^s)))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 3948 2977.3
## 2 3947 2974.4 1 2.88375 3.8267 0.05051 .
## 3 3947 2975.3 0 0.00000
## 4 3946 2974.4 1 0.87017 1.1544 0.28269
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 3 14629.60
## 2 3a 14627.77
## 3 3b 14628.90
## 4 3c 14629.75##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (p * A + ((1 -
## p) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 0.897226 0.260481 3.444 0.000578 ***
## phi 0.015087 0.005705 2.644 0.008217 **
## alpha 0.445392 0.089305 4.987 6.39e-07 ***
## A 2.798841 0.137033 20.425 < 2e-16 ***
## k 4.938187 2.215835 2.229 0.025897 *
## p 0.346373 0.141286 2.452 0.014266 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8681 on 3947 degrees of freedom
##
## Number of iterations to convergence: 4
## Achieved convergence tolerance: 2.77e-06
## (13 observations deleted due to missingness)summary
- add p model: fits
- add s model: fits
- add s+p model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.98118, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -7.4445, p-value = 9.73e-14
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 6 rows containing missing values (geom_point).## Warning: Removed 1108 row(s) containing missing values (geom_path).
plotting 2
M221 - Eastern Broadleaf Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 4659 10327
## 2 4658 10325 1 2.481 1.1194 0.2901
## 3 4657 10235 1 89.688 40.8084 1.843e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 23096.71
## 2 2 23097.59
## 3 3 23058.91##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -0.081319 0.221554 -0.367 0.714
## phi -0.009838 0.009441 -1.042 0.297
## alpha 0.808623 0.121451 6.658 3.10e-11 ***
## A 4.344956 0.212225 20.473 < 2e-16 ***
## k 9.583534 2.270030 4.222 2.47e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.482 on 4657 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 5.044e-06
## (26 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_M221, :
## number of iterations exceeded maximum of 50
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M221, :
## step factor 0.000488281 reduced below 'minFactor' of 0.000976562
## Error in numericDeriv(form[[3L]], names(ind), env, central = nDcentral) :
## Missing value or an infinity produced when evaluating the model
## model AIC
## 1 3 23058.91
## 2 3a NA
## 3 3b NA
## 4 3c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -0.081319 0.221554 -0.367 0.714
## phi -0.009838 0.009441 -1.042 0.297
## alpha 0.808623 0.121451 6.658 3.10e-11 ***
## A 4.344956 0.212225 20.473 < 2e-16 ***
## k 9.583534 2.270030 4.222 2.47e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.482 on 4657 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 5.044e-06
## (26 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.87004, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -6.6975, p-value = 2.12e-11
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 18 rows containing missing values (geom_point).## Warning: Removed 982 row(s) containing missing values (geom_path).
plotting 2
M223 - Ozark Broadleaf Forest Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 524 819.79
## 2 523 819.72 1 0.0704 0.0449 0.83225
## 3 522 814.78 1 4.9447 3.1679 0.07568 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 2249.276
## 2 2 2251.231
## 3 3 2250.042##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * A *
## B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 2.6216 1.6056 1.633 0.1031
## A 2.0517 0.4973 4.126 4.3e-05 ***
## k 19.1597 10.5392 1.818 0.0696 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.251 on 524 degrees of freedom
##
## Number of iterations to convergence: 10
## Achieved convergence tolerance: 6.363e-06
## (3 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M223, :
## number of iterations exceeded maximum of 50
## Error in numericDeriv(form[[3L]], names(ind), env, central = nDcentral) :
## Missing value or an infinity produced when evaluating the model
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (p * A + ((1 - p) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 524 819.79
## 2 523 816.31 1 3.4847 2.2326 0.1357
## model AIC
## 1 1 2249.276
## 2 1a 2249.031
## 3 1b NA
## 4 1c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (p *
## A + ((1 - p) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 2.6617 1.6208 1.642 0.101
## A 3.6417 4.4794 0.813 0.417
## k 344.4875 976.8539 0.353 0.724
## p 0.3030 0.2936 1.032 0.303
##
## Residual standard error: 1.249 on 523 degrees of freedom
##
## Number of iterations to convergence: 9
## Achieved convergence tolerance: 5.736e-06
## (3 observations deleted due to missingness)summary
- add p model: fits
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.92549, p-value = 1.685e-15
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -1.8578, p-value = 0.0632
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1175 row(s) containing missing values (geom_path).
plotting 2
M231 - Ouachita Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 581 732.19
## 2 580 720.59 1 11.6017 9.3382 0.002347 **
## 3 579 714.19 1 6.3998 5.1884 0.023102 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 2364.027
## 2 2 2356.699
## 3 3 2353.489##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 2.72388 2.02426 1.346 0.178953
## phi 0.07373 0.03230 2.283 0.022807 *
## alpha 0.88790 0.36882 2.407 0.016379 *
## A 2.11860 0.59337 3.570 0.000386 ***
## k 20.19953 6.69006 3.019 0.002645 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.111 on 579 degrees of freedom
##
## Number of iterations to convergence: 22
## Achieved convergence tolerance: 8.836e-06
## (4 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
model selection 2
## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_M231, :
## number of iterations exceeded maximum of 50
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M231, :
## step factor 0.000488281 reduced below 'minFactor' of 0.000976562
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_M231, :
## step factor 0.000488281 reduced below 'minFactor' of 0.000976562
## model AIC
## 1 3 2353.489
## 2 3a NA
## 3 3b NA
## 4 3c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 2.72388 2.02426 1.346 0.178953
## phi 0.07373 0.03230 2.283 0.022807 *
## alpha 0.88790 0.36882 2.407 0.016379 *
## A 2.11860 0.59337 3.570 0.000386 ***
## k 20.19953 6.69006 3.019 0.002645 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.111 on 579 degrees of freedom
##
## Number of iterations to convergence: 22
## Achieved convergence tolerance: 8.836e-06
## (4 observations deleted due to missingness)summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.97154, p-value = 3.181e-09
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -3.6475, p-value = 0.0002648
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 2 rows containing missing values (geom_point).## Warning: Removed 1218 row(s) containing missing values (geom_path).
plotting 2
M242 - Cascade Mixed Forest
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M261 - Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 156 186.49
## 2 155 176.83 1 9.6612 8.4687 0.004145 **
## 3 154 172.37 1 4.4610 3.9857 0.047651 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 555.0482
## 2 2 548.5899
## 3 3 546.5272##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -0.68834 4.20877 -0.164 0.870301
## phi 0.16904 0.04236 3.991 0.000102 ***
## alpha 0.80138 0.37035 2.164 0.032018 *
## A 14.44405 17.75070 0.814 0.417063
## k 322.91440 267.39188 1.208 0.229035
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.058 on 154 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 5.138e-06
## (163 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
model selection 2
## Error in numericDeriv(form[[3L]], names(ind), env, central = nDcentral) :
## Missing value or an infinity produced when evaluating the model
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (p * A + ((1 - p) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 154 172.37
## 2 153 171.91 1 0.45685 0.4066 0.5247
## model AIC
## 1 3 546.5272
## 2 3a 548.1052
## 3 3b 548.4791
## 4 3c NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -0.68834 4.20877 -0.164 0.870301
## phi 0.16904 0.04236 3.991 0.000102 ***
## alpha 0.80138 0.37035 2.164 0.032018 *
## A 14.44405 17.75070 0.814 0.417063
## k 322.91440 267.39188 1.208 0.229035
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.058 on 154 degrees of freedom
##
## Number of iterations to convergence: 7
## Achieved convergence tolerance: 5.138e-06
## (163 observations deleted due to missingness)summary
- add p model: fits
- add s model: fits
- add s+p model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.96832, p-value = 0.001033
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = 1.977, p-value = 0.04804
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 90 rows containing missing values (geom_point).## Warning: Removed 1274 row(s) containing missing values (geom_path).
plotting 2
M262 - Califormia Coastal Range = Coniferous Forest - Open woodland Shrub Meadow
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M313 - Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow
model selection 1
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## model AIC
## 1 1 NA
## 2 2 NA
## 3 3 NA## Warning in min(AIC1_M313$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_M313.' not foundsummary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M331 - Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow
model selection 1
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## model AIC
## 1 1 NA
## 2 2 NA
## 3 3 NA## Warning in min(AIC1_M331$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_M331.' not foundsummary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M332 - Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M333 - Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M334 - Black Hills Coniferous Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 180 187.48
## 2 179 187.45 1 0.0346 0.033 0.856041
## 3 178 176.90 1 10.5461 10.612 0.001346 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 561.9578
## 2 2 563.9241
## 3 3 555.3271##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k +
## B_plt_t1_MgHa)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 5.34143 10.68436 0.500 0.617742
## phi -0.04759 0.05520 -0.862 0.389777
## alpha 0.90924 0.24954 3.644 0.000352 ***
## A 1.29761 1.34860 0.962 0.337258
## k 41.52941 17.19855 2.415 0.016760 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9969 on 178 degrees of freedom
##
## Number of iterations to convergence: 9
## Achieved convergence tolerance: 6.029e-06
## (75 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
model selection 2
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (p * A + ((1 - p) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)))
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (A * B_plt_t1_MgHa^s/(k^s + B_plt_t1_MgHa^s))
## Model 4: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (p * A + ((1 - p) * A * B_plt_t1_MgHa^s/(k^s + B_plt_t1_MgHa^s)))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 178 176.90
## 2 177 176.82 1 0.08245 0.0825 0.77422
## 3 177 175.67 0 0.00000
## 4 176 172.74 1 2.93465 2.9901 0.08553 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 3 555.3271
## 2 3a 557.2418
## 3 3b 556.0501
## 4 3c 554.9672##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (p * A + ((1 -
## p) * A * B_plt_t1_MgHa^s/(k^s + B_plt_t1_MgHa^s)))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 4.24357 8.41669 0.504 0.614762
## phi -0.04452 0.05422 -0.821 0.412714
## alpha 0.93202 0.24607 3.788 0.000209 ***
## A 1.08465 0.98015 1.107 0.269972
## k 35.67916 6.75553 5.281 3.75e-07 ***
## p 0.22887 0.10184 2.247 0.025852 *
## s 3.22389 1.93656 1.665 0.097742 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9907 on 176 degrees of freedom
##
## Number of iterations to convergence: 10
## Achieved convergence tolerance: 5.967e-06
## (75 observations deleted due to missingness)summary
- add p model: fits
- add s model: fits
- add s+p model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.72367, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -0.21879, p-value = 0.8268
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 33 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_segment).## Warning: Removed 1264 row(s) containing missing values (geom_path).
plotting 2
## Warning: Removed 1 rows containing missing values (geom_segment).
M341 - Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
model selection 2
summary
- add p model: does not fit
- add s model: does not fit
- add s+p model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
Fitted parameters
Best / selected models by ecoprovince
| Code | Ecoregion | Sel.Mod |
|---|---|---|
| 211 | Northeastern Mixed Forest | 3b |
| 212 | Laurentian Mixed Forest | 3a |
| 221 | Eastern Broadleaf Forest | 3a |
| 222 | Midwest Broadleaf Forest | 3a |
| 223 | Central Interior Broadleaf Forest | 3 |
| 231 | Southeastern Mixed Forest | 3b |
| 232 | Outer Coastal Plain Mixed Forest | 3a |
| 234 | Lower Mississippi Riverine Forest | 1 |
| 242 | Pacific Lowland Mixed Forest | NA |
| 251 | Prairie Parkland (Temperate) | 1 |
| 255 | Prairie Parkland (Subtropical) | NA |
| 261 | California Coastal Chaparral Forest and Shrub | NA |
| 262 | California Dry Steppe | NA |
| 263 | California Coastal Steppe - Mixed Forest and Redwood Forest | NA |
| 313 | Colorado Plateau Semi-Desert | NA |
| 315 | Southwest Plateau and Plains Dry Steppe and Shrub | NA |
| 321 | Chihuahuan Semi-Desert | NA |
| 322 | American Semidesert and Desert | NA |
| 331 | Great Plains/Palouse Dry Steppe | 3 |
| 332 | Great Plains Steppe | 1a |
| 341 | Intermountain Semi-Desert and Desert | NA |
| 342 | Intermountain Semi-Desert | NA |
| 411 | Everglades | NA |
| M211 | Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow | 3a |
| M221 | Central Appalachian Broadleaf Forest - Coniferous Forest - Meadow | 3 |
| M223 | Ozark Broadleaf Forest Meadow | 1a |
| M231 | Ouachita Mixed Forest | 3 |
| M242 | Cascade Mixed Forest | NA |
| M261 | Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow | 3 |
| M262 | California Coastal Range Coniferous Forest - Open Woodland - Shrub - Meadow | NA |
| M313 | Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow | NA |
| M331 | Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow | NA |
| M332 | Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | NA |
| M333 | Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | NA |
| M334 | Black Hills Coniferous Forest | 3c |
| M341 | Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow | NA |
table by ecoprovince
| Code | Ecoregion | region | n.obs | n.plots | ge | ge.variance | ge.2.5 | ge.97.5 | phi | phi.variance | phi.2.5 | phi.97.5 | alpha | alpha.variance | alpha.2.5 | alpha.97.5 | A | A.2.5 | A.97.5 | k | k.2.5 | k.97.5 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 211 | Northeastern Mixed Forest | east | 4834 | 2417 | -0.0701777 | 0.0378473 | -0.4516004 | 0.3112450 | 0.0081316 | 0.0000419 | -0.0045648 | 0.0208281 | 0.5233081 | 0.0094110 | 0.3331092 | 0.7135070 | 3.8560322 | 3.3955220 | 4.316542 | 7.2130845 | 4.9140132 | 9.5121557 |
| 212 | Laurentian Mixed Forest | east | 12976 | 6488 | 0.6460311 | 0.0434727 | 0.2373137 | 1.0547485 | 0.0171509 | 0.0000238 | 0.0075877 | 0.0267141 | 0.5747427 | 0.0032763 | 0.4625398 | 0.6869456 | 2.9832899 | 2.7390098 | 3.227570 | 25.1990149 | 19.7462922 | 30.6517376 |
| 221 | Eastern Broadleaf Forest | east | 5462 | 2731 | -0.9996150 | 0.0207816 | -1.2822371 | -0.7169930 | 0.0182832 | 0.0000521 | 0.0041306 | 0.0324358 | 0.5862302 | 0.0074067 | 0.4175057 | 0.7549547 | 7.6162619 | 5.7511807 | 9.481343 | 199.6435884 | 41.8356816 | 357.4514952 |
| 222 | Midwest Broadleaf Forest | east | 3554 | 1777 | -0.3371896 | 0.0833952 | -0.9035127 | 0.2291336 | 0.0166786 | 0.0001668 | -0.0086511 | 0.0420083 | 0.7313236 | 0.0080930 | 0.5549034 | 0.9077438 | 6.1529187 | 4.9328446 | 7.372993 | 112.1183167 | 55.6243232 | 168.6123103 |
| 223 | Central Interior Broadleaf Forest | east | 6390 | 3195 | -1.0271460 | 0.0219393 | -1.3175365 | -0.7367556 | -0.0185398 | 0.0000921 | -0.0373497 | 0.0002701 | 0.5344246 | 0.0094325 | 0.3440166 | 0.7248326 | 5.6438245 | 5.1405654 | 6.147083 | 33.3069888 | 25.7469229 | 40.8670546 |
| 231 | Southeastern Mixed Forest | east | 8200 | 4100 | 0.5717335 | 0.0376410 | 0.1914023 | 0.9520647 | 0.0041198 | 0.0000443 | -0.0089209 | 0.0171605 | 0.7207891 | 0.0058020 | 0.5714682 | 0.8701099 | 8.1321892 | 0.8942751 | 15.370103 | 32.2539036 | -234.5665566 | 299.0743637 |
| 232 | Outer Coastal Plain Mixed Forest | east | 8194 | 4097 | 0.4993901 | 0.0462372 | 0.0778623 | 0.9209178 | -0.0029963 | 0.0000463 | -0.0163320 | 0.0103395 | 0.6469230 | 0.0043235 | 0.5180241 | 0.7758220 | 5.3376805 | 4.8148740 | 5.860487 | 40.9241269 | 25.0560111 | 56.7922428 |
| 234 | Lower Mississippi Riverine Forest | east | 862 | 431 | 0.3086104 | 0.5636793 | -1.1655112 | 1.7827320 | NA | NA | NA | NA | NA | NA | NA | NA | 3.9841392 | 2.7849147 | 5.183364 | 0.0839457 | -0.3513901 | 0.5192815 |
| 242 | Pacific Lowland Mixed Forest | pacific | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 251 | Prairie Parkland (Temperate) | east | 1394 | 697 | -0.2615823 | 0.2834626 | -1.3065478 | 0.7833832 | NA | NA | NA | NA | NA | NA | NA | NA | 3.4267952 | 2.5608351 | 4.292755 | 12.2112022 | 4.0960249 | 20.3263795 |
| 255 | Prairie Parkland (Subtropical) | east | 446 | 223 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 261 | California Coastal Chaparral Forest and Shrub | pacific | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 262 | California Dry Steppe | pacific | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 263 | California Coastal Steppe - Mixed Forest and Redwood Forest | pacific | 4 | 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 313 | Colorado Plateau Semi-Desert | interior west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 315 | Southwest Plateau and Plains Dry Steppe and Shrub | interior west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 321 | Chihuahuan Semi-Desert | interior west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 322 | American Semidesert and Desert | interior west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 331 | Great Plains/Palouse Dry Steppe | interior west | 118 | 59 | 0.8753568 | 104.2515550 | -19.3768540 | 21.1275675 | 0.2478384 | 0.0267117 | -0.0763383 | 0.5720151 | 1.0137067 | 0.3387254 | -0.1406899 | 2.1681033 | 0.7672107 | -1.8583073 | 3.392729 | 8.1068470 | -17.5967287 | 33.8104226 |
| 332 | Great Plains Steppe | interior west | 154 | 77 | -0.1567804 | 1.6399531 | -2.6908696 | 2.3773089 | NA | NA | NA | NA | NA | NA | NA | NA | 5.2350165 | 0.2501706 | 10.219862 | 137.1513327 | -89.0640077 | 363.3666731 |
| 341 | Intermountain Semi-Desert and Desert | interior west | 4 | 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 342 | Intermountain Semi-Desert | interior west | 2 | 1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 411 | Everglades | east | 66 | 33 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M211 | Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow | east | 5104 | 2552 | 0.8972259 | 0.0678505 | 0.3865352 | 1.4079167 | 0.0150869 | 0.0000326 | 0.0039012 | 0.0262725 | 0.4453919 | 0.0079754 | 0.2703037 | 0.6204800 | 2.7988408 | 2.5301790 | 3.067503 | 4.9381866 | 0.5938972 | 9.2824760 |
| M221 | Central Appalachian Broadleaf Forest - Coniferous Forest - Meadow | east | 5262 | 2631 | -0.0813187 | 0.0490861 | -0.5156692 | 0.3530318 | -0.0098383 | 0.0000891 | -0.0283479 | 0.0086713 | 0.8086234 | 0.0147504 | 0.5705216 | 1.0467251 | 4.3449555 | 3.9288933 | 4.761018 | 9.5835344 | 5.1332003 | 14.0338684 |
| M223 | Ozark Broadleaf Forest Meadow | east | 604 | 302 | 2.6616500 | 2.6268365 | -0.5223335 | 5.8456335 | NA | NA | NA | NA | NA | NA | NA | NA | 3.6416527 | -5.1580948 | 12.441400 | 344.4875424 | -1574.5518250 | 2263.5269097 |
| M231 | Ouachita Mixed Forest | east | 678 | 339 | 2.7238765 | 4.0976205 | -1.2519071 | 6.6996602 | 0.0737275 | 0.0010431 | 0.0102925 | 0.1371624 | 0.8879001 | 0.1360306 | 0.1635056 | 1.6122946 | 2.1186016 | 0.9531822 | 3.284021 | 20.1995348 | 7.0597838 | 33.3392857 |
| M242 | Cascade Mixed Forest | pacific | 34 | 17 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M261 | Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow | pacific | 340 | 170 | -0.6883394 | 17.7137530 | -9.0027162 | 7.6260374 | 0.1690382 | 0.0017940 | 0.0853659 | 0.2527105 | 0.8013844 | 0.1371615 | 0.0697561 | 1.5330127 | 14.4440524 | -20.6222516 | 49.510356 | 322.9143977 | -205.3150747 | 851.1438701 |
| M262 | California Coastal Range Coniferous Forest - Open Woodland - Shrub - Meadow | interior west | 8 | 4 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M313 | Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow | interior west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M331 | Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow | interior west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M332 | Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | interior west | 20 | 10 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M333 | Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | interior west | 22 | 11 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M334 | Black Hills Coniferous Forest | interior west | 310 | 155 | 4.2435694 | 70.8407109 | -12.3670625 | 20.8542013 | -0.0445205 | 0.0029400 | -0.1515296 | 0.0624887 | 0.9320177 | 0.0605519 | 0.4463843 | 1.4176510 | 1.0846462 | -0.8497134 | 3.019006 | 35.6791576 | 22.3468885 | 49.0114267 |
| M341 | Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow | interior west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
plot ge
map
## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\hogan.jaaron\Dropbox\FIA_R\Mapping\S_USA.EcoMapProvinces\S_USA.EcoMapProvinces.shp", layer: "S_USA.EcoMapProvinces"
## with 37 features
## It has 17 fields
## Integer64 fields read as strings: PROVINCE_ PROVINCE_I## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
plot phi (effect of DeltaPDSI)
plot alpha (biomass growth compensation effect)
plot A (asymptote of forest biomass growth in Mg/ha/yr)
## Warning: Removed 19 rows containing missing values (geom_point).
plot k (stand biomass at half biomss G in Mg/ha)
## Warning: Removed 19 rows containing missing values (geom_point).
Caclulations - weighted averages
ge (stand biomass growth enhancement factor in % 2000-2021)
## region weighted.ge weighted.ge.std_Error 95 % CI, upper
## 1 entire US 0.188648990 0.08924345 0.36356614
## 2 pacific -0.003598219 0.02200089 0.03952353
## 3 east 0.170804842 0.07428861 0.31641052
## 4 interior west 0.021442367 0.04428945 0.10824968
## 95 % CI, lower
## 1 0.01373184
## 2 -0.04671997
## 3 0.02519916
## 4 -0.06536495phi (effect of DeltaPDSI)
## region weighted.phi weighted.phi.std_Error 95 % CI, upper
## 1 entire US 0.0070707690 0.0023391806 0.011655563
## 2 pacific 0.0008836290 0.0002214075 0.001317588
## 3 east 0.0059496998 0.0022952212 0.010448333
## 4 interior west 0.0002374402 0.0003933247 0.001008357
## 95 % CI, lower
## 1 0.0024859751
## 2 0.0004496703
## 3 0.0014510663
## 4 -0.0005334762alpha (biomass growth compensation effect)
## region weighted.alpha weighted.alpha.std_Error 95 % CI, upper
## 1 entire US 0.587716290 0.026025563 0.638726393
## 2 pacific 0.004189150 0.001935981 0.007983673
## 3 east 0.577245924 0.025905434 0.628020575
## 4 interior west 0.006281216 0.001578093 0.009374278
## 95 % CI, lower
## 1 0.5367061877
## 2 0.0003946271
## 3 0.5264712737
## 4 0.0031881538A (asymptote of forest biomass growth in Mg/ha/yr)
## region weighted.A
## 1 entire US 4.957463
## 2 pacific 12.992005
## 3 east 4.940171
## 4 interior west 1.932545K (stand biomass at half biomass G in Mg/ha)
## region weighted.k
## 1 entire US 47.98194
## 2 pacific 290.45210
## 3 east 46.51098
## 4 interior west 51.94115